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A Review of Meta-Analysis Packages in R

Author

Listed:
  • Joshua R. Polanin

    (Development Services Group, Inc.)

  • Emily A. Hennessy
  • Emily E. Tanner-Smith

    (Vanderbilt University)

Abstract

Meta-analysis is a statistical technique that allows an analyst to synthesize effect sizes from multiple primary studies. To estimate meta-analysis models, the open-source statistical environment R is quickly becoming a popular choice. The meta-analytic community has contributed to this growth by developing numerous packages specific to meta-analysis. The purpose of this study is to locate all publicly available meta-analytic R packages. We located 63 packages via a comprehensive online search. To help elucidate these functionalities to the field, we describe each of the packages, recommend applications for researchers interested in using R for meta-analyses, provide a brief tutorial of two meta-analysis packages, and make suggestions for future meta-analytic R package creators.

Suggested Citation

  • Joshua R. Polanin & Emily A. Hennessy & Emily E. Tanner-Smith, 2017. "A Review of Meta-Analysis Packages in R," Journal of Educational and Behavioral Statistics, , vol. 42(2), pages 206-242, April.
    • Handle: RePEc:sae:jedbes:v:42:y:2017:i:2:p:206-242
    DOI: 10.3102/1076998616674315
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    References listed on IDEAS

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    1. Jack Vevea & Larry Hedges, 1995. "A general linear model for estimating effect size in the presence of publication bias," Psychometrika, Springer;The Psychometric Society, vol. 60(3), pages 419-435, September.
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    Cited by:

    1. Diego A Forero & Sandra Lopez-Leon & Yeimy González-Giraldo & Pantelis G Bagos, 2019. "Ten simple rules for carrying out and writing meta-analyses," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-7, May.
    2. Christopher Hansen & Holger Steinmetz & Jörn Block, 2022. "How to conduct a meta-analysis in eight steps: a practical guide," Management Review Quarterly, Springer, vol. 72(1), pages 1-19, February.

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